Prediction and optimization of biodiesel production by using ANN and RSM

Yıl 2022, , 53 - 62, 01.07.2022

Ceyla Özgür

https://doi.org/10.18245/ijaet.1057170

Cited By: 1

Öz

This experimental work examined the prediction and optimization of biodiesel production from pomegranate seed oil using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) with central composite design and The transesterification method chosen for biodiesel production. The Central Composite Design (CCD) optimization conditions were methanol/oil molar ratio (3:1 to 11:1), catalyst rate (0.5 wt% to 1.50 wt%), temperature (50 ℃ to 70 ℃) and time (45 min to 105 min). The process factors were optimized by using CCD based on the RSM method and developed an ANN model to predict biodiesel yield. The optimum yield was found 95.68% with optimum process parameters as 8.01:1 methanol/oil molar ratio, 1.08 wt% catalyst rate, 70 ℃ temperature and 45 min time. The coefficient of determination (R2) acquired from the response surface methodology model is 0.9887 and is better when compared to the coefficient of determination (R2) of 0.9691 acquired from the Artificial neural network model. According to the results, using RSM and ANN models is beneficial for optimizing and predicting the biodiesel production process.

Anahtar Kelimeler

ANN, optimization, prediction, pomegranate biodiesel, RSM

Kaynakça

• Mansourpoor M, Shariat A. Optimization of Biodiesel Production from Sunflower Oil Using Response Surface Methodology. Journal of Chemical Engineering & Process Technology, 3, pp. 141, 2012.
• Singh Y, Singla, A. Comparative analysis of jatropha and karanja-based biodiesel properties, performance and exhaust emission characteristics in an unmodified diesel engine. Pollution, 1, pp. 23-30, 2015.
• Haryanto A, Saputra, TW, Telaumbanua M., Gita AC. Application of Artificial Neural Network to Predict Biodiesel Yield from Waste Frying Oil Transesterification. Indonesian Journal of Science and Technology, 5, pp. 62-74, 2020.
• Anbessa TT, Karthikeyan S. Optimization and Mathematical Modeling of Biodiesel Production using Homogenous Catalyst from Waste Cooking Oil. International Journal of Engineering and Advanced Technology, 9, pp. 1733-1739, 2019.
• Moradi GR, Dehghani S, Khosravian F, Arjmandzadeh A. The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield. Renewable Energy, 50, pp. 915-920, 2013.
• Hatefi H, Mohsennia M, Niknafs H., Golzary A. Catalytic production of biodiesel from corn oil by metal-mixed oxides. Pollution, 3, pp. 679-688, 2017.
• Kumar S. Comparison of linear regression and artificial neural network technique for prediction of a soybean biodiesel yield Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42, pp. 1425-1435, 2020.
• Esonye C, Onukwuli OD, Ofoefule AU. Optimization of methyl ester production from Prunus Amygdalus seed oil using response surface methodology and Artificial Neural Networks. Renewable Energy, 130, pp. 61-72, 2019.
• Betiku E, Omilakin OR, Ajala SO, Okeleye AA, Taiwo A., Solomon BO. Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis. Energy, 72, pp. 266-273, 2014.
• Betiku E, Adepoju TF, Omole AK, Aluko SE. Statistical approach to the optimization of oil from Beniseed (Sesamum indicum) Oilseeds. Journal of Food Science and Engineering, 2, pp. 351-357, 2012.
• Farobie O, Hasanah N, Matsumura Y. Artificial neural network modeling to predict biodiesel production in supercritical methanol and ethanol using spiral reactor. Procedia Environmental Sciences, 28, pp. 214-223, 2015.
• Thoai DN, Tongurai C, Prasertsit K, Kumar A. Predictive Capability Evaluation of RSM and ANN in Modeling and Optimization of Biodiesel Production from Palm (Elaeisguineensis) Oil. International Journal of Applied Engineering Research, 13, pp. 7529-7540, 2018.
• Ude CN, Onukwuli OD, Nwobi–Okoye C, Anisiji OE, Atuanya CU, Menkit MC. Performance evaluation of cottonseed oil methyl esters produced using CaO and prediction with an artificial neural network. Biofuels, 11, pp. 77-84, 2020.
• Matei D, Doicin B, Cursaru D, Ezeanu DD. Yield Optimization Using Artificial Neural Networks in Biodiesel Production from Soybean Oil. Revista de Chimie, 71, pp. 132-140, 2020.
• Sivamani S, Selvakumar S, Rajendran K, Muthusamy S. Artificial neural network–genetic algorithmbased optimization of biodiesel production from Simarouba glauca. Biofuels, 10, pp. 393-401, 2019.
• Chakraborty R, Sahu H. Intensification of biodiesel production from waste goat tallow using infrared radiation: Process evaluation through response surface methodology and artificial neural network. Applied Energy, 114, pp. 827-836, 2014.
• Samuel OD, Okwu MO. Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,41, pp. 1049-1061, 2019.
• Gautam R, Ansari N, Sharma A, Singh Y. Development of the Ethyl Ester from Jatropa Oil through Response Surface Methodology Approach. Pollution, 6, pp. 135-147, 2020.
• Naidoo R, Sithole B, Obwaka E. Using Response Surface Methodology in optimization of biodiesel production via alkali catalysed transesterification of waste cooking oil. Journal of Scientific and Industrial Research, 75, pp. 188-193, 2016.
• Kumar S, Jain S, Kumar H. Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41, pp. 1285-1295, 2019.
• Ayola AA, Hymore FK, Omonhinmin CA, Babalola PO, Bolujo EO, Adeyemi, GA, Babalola R, Olafadehan OA. Data on artificial neural network and response surface methodology analysis of biodiesel production. Data in Brief, 31, pp. 105726, 2020.
• Soji-Adekunle AR, Asere AA, Ishola NB, Oloko-Oba IM, Betiku E. Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology. International Journal of Ambient Energy, 40, pp. 716-725, 2019.
• Bharadwaj AVSL, Sai. Niju S, Begum KMM, Anantharaman N. Optimization and modeling of biodiesel production using fluorite as a heterogeneous catalyst. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41, pp. 1862-1878, 2019.
• Elkelawy M, Bastawissi HAE, Esmaeil KK, Radwan AM, Panchal H, Sadasivuni KK, Suresh M, Israr M. Maximization of biodiesel production from sunflower and soybean oils and prediction of diesel engine performance and emission characteristics through response surface methodology. Fuel, 266, pp. 17072, 2020.
• Gürgen S, Ünver B, Altın I. Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network. Renewable Energy, 117, pp. 538-544, 2018.
• Bilgili M, Sahin B, Yasar A. Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy, 32, pp. 2350-2360, 2007.
• Al-Shanableh F, Evcil A, Savaş MA. Prediction of cold flow properties of biodiesel fuel using artificial neural network. Procedia Computer Science, 102, pp. 273-280, 2016.
• Chinyere Ezekannagha B, Callistus Ude N, Okechukwu Onukwuli D. Optimization of the methanolysis of lard oil in the production of biodiesel with response surface methodology. Egyptian Journal of Petroleum, 26, pp.1001-1011, 2017.
• Soji-Adekunlea AR, Asere, AA, Ishola NB, Oloko-Oba IM, Betiku E. Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology. International Journal of Ambient Energy, 40, pp. 716-725, 2019.